learning_ai_common_plat/docs/devops/SINGLE_VM_DEPLOYMENT.md

ByteLyst Ecosystem — Single-VM Deployment Guide

Deploy the entire ByteLyst ecosystem on one VM, fully Dockerized, with a local Kubernetes layer (Docker Desktop or K3s) for production-readiness practice.

---

Package-Manager Strategy

• learning_ai_common_plat is the canonical pnpm workspace monorepo for shared packages, services, and dashboards.
• All 10 Node/TypeScript product repos use pnpm with pnpm-lock.yaml.
• Product repos remain independent repositories, not one combined workspace.
• All repos consume @bytelyst/* packages from the local Gitea npm registry (49 packages published).
• All Dockerfiles use pnpm + BuildKit secret mount for registry authentication.

---

1. Service Inventory

Shared Infrastructure (common-plat)

Service	Port	Image	RAM Est.
platform-service	4003	Fastify 5 + TS	~200 MB
extraction-service	4005	Fastify 5 + Python sidecar	~350 MB
mcp-server	4007	Fastify 5 + TS	~150 MB
Cosmos DB Emulator	8081, 1234	mcr.microsoft.com/cosmosdb/linux/azure-cosmos-emulator:vnext-preview	~2 GB
Azurite (blob)	10000	mcr.microsoft.com/azure-storage/azurite	~100 MB
Mailpit (SMTP)	1025, 8025	axllent/mailpit	~50 MB
Traefik (gateway)	80, 8080	traefik:v3.3	~100 MB
Loki (logs)	3100	grafana/loki	~200 MB
Grafana (dashboards)	3000	grafana/grafana	~200 MB
Gitea (git + CI + pkg registry)	3300, 2222	gitea/gitea	~150 MB
act_runner (CI runner)	—	gitea/act_runner (or host-mode binary)	~100 MB

Product Backends (Fastify 5 + TypeScript)

Product	Port	RAM Est.
LysnrAI backend	4015	~150 MB
MindLyst backend	4014	~150 MB
ChronoMind backend	4011	~150 MB
JarvisJr backend	4012	~150 MB
NomGap backend	4013	~150 MB
PeakPulse backend	4010	~150 MB
FlowMonk backend	4017	~150 MB
NoteLett backend	4016	~150 MB
ActionTrail backend	4018	~150 MB
LocalMemGPT backend	4019	~150 MB

Web Dashboards (Next.js 16)

Dashboard	Default Port	Compose Port	RAM Est.	Notes
admin-web	3000	3001	~250 MB	No port in package.json; must set PORT=3001 env
user-dashboard-web	3002	3002	~250 MB	Port set in package.json
tracker-web	3003	3003	~200 MB	Port set in package.json
NomGap web	3040	3040	~200 MB	Port set in Dockerfile
ChronoMind web	3000	3051	~200 MB	No port override; must set PORT env
JarvisJr web	3000	3052	~200 MB	No port override; must set PORT env
FlowMonk web	3000	3053	~200 MB	No port override; must set PORT env
NoteLett web	3000	3054	~200 MB	Dockerfile EXPOSE 3000; remap in compose
ActionTrail web	3000	3060	~200 MB	Dockerfile EXPOSE 3000; remap in compose
LocalMemGPT web	3070	3070	~200 MB	Port set in package.json + Dockerfile
MindLyst web	3050	3050	~200 MB	Port set in package.json (-p 3050)

Port conflict warning: Grafana uses port 3000. admin-web, ChronoMind, JarvisJr, FlowMonk, NoteLett, and ActionTrail webs all default to 3000. The compose file must either set PORT env var or remap via ports: mapping.

Optional / AI

Service	Port	RAM Est.
Ollama (LLM)	11434	4–16 GB (model-dependent)

---

2. VM Sizing

Minimum (dev/staging, no Ollama)

Spec	Value
vCPUs	8
RAM	32 GB
Disk	100 GB SSD
OS	Ubuntu 24.04 LTS

Breakdown:

• Cosmos Emulator: ~2 GB
• 10 Fastify backends × 150 MB = ~1.5 GB
• 3 shared services × 250 MB = ~0.75 GB
• 11 Next.js webs × 200 MB = ~2.2 GB
• Infra (Traefik, Loki, Grafana, Azurite, Mailpit) = ~0.65 GB
• Gitea + act_runner = ~0.25 GB
• K3s overhead = ~0.5 GB
• Subtotal: ~7.65 GB → headroom for spikes + build cache = 32 GB

Recommended (with Ollama, small models)

Spec	Value
vCPUs	16
RAM	64 GB
Disk	200 GB NVMe SSD
GPU	Optional NVIDIA T4/A10 for fast LLM inference
OS	Ubuntu 24.04 LTS

Cloud Equivalents

Provider	Instance	vCPU	RAM	Price (approx)
Azure	Standard_D8s_v5	8	32 GB	~$280/mo
Azure	Standard_D16s_v5	16	64 GB	~$560/mo
AWS	m6i.2xlarge	8	32 GB	~$280/mo
AWS	m6i.4xlarge	16	64 GB	~$560/mo
Hetzner	CPX51	16	32 GB	~$45/mo
Hetzner	CCX63	48	192 GB	~$230/mo
Home	Mac Mini M4 Pro	12	48 GB	One-time ~$1,600

Cost tip: Hetzner is 5–10× cheaper than Azure/AWS for dev/staging.

---

3. Architecture: Docker Compose → K3s Migration Path

Phase 1: Docker Compose

📋 Enhanced plan: See SINGLE_VM_ENHANCED_PLAN.md for the deployment plan with Coolify, Valkey, Uptime Kuma, and other open-source tooling.

Create a unified docker-compose.ecosystem.yml that brings everything up.

Phase 2: Local Kubernetes (Docker Desktop or K3s)

Two options for single-node K8s — both give you real kubectl, Helm, Ingress, and CRDs identical to production AKS/EKS/GKE.

Option A: Docker Desktop Kubernetes (recommended for Mac/Windows dev)

Docker Desktop includes a built-in kind (Kubernetes IN Docker) cluster. Enable it in Docker Desktop → Settings → Kubernetes → Enable Kubernetes.

• Zero install — checkbox in Docker Desktop, K8s v1.31+ included
• Images shared — docker build images are immediately available to K8s (no import step!)
• GUI dashboard — Docker Desktop shows Deployments, Pods, Services, Ingresses, ConfigMaps, Secrets
• kubectl pre-configured — context docker-desktop auto-created
• Helm works — install via brew install helm
• Best for: Mac/Windows local development, quick iteration, visual debugging
• Limitation: Single-node only, can't add workers (use K3s for multi-node practice)

Option B: K3s (recommended for Linux VMs / multi-node practice)

K3s is a lightweight, certified Kubernetes distro.

• Production-grade (CNCF certified, used by Rancher)
• Single binary, ~70 MB, installs in 30 seconds
• Built-in Traefik Ingress (you already use Traefik!)
• Built-in local-path StorageClass
• Runs as systemd service (survives reboot)
• Can scale to multi-node later by just joining worker nodes
• Best for: Linux VMs, Hetzner/cloud deployment, multi-node scaling practice

---

4. Implementation Plan

4.1 Phase 1 — Unified Docker Compose

Create docker-compose.ecosystem.yml at workspace root (~/code/mygh/) that composes all services:

# ~/code/mygh/docker-compose.ecosystem.yml
# All repos consume @bytelyst/* from local Gitea npm registry.

services:
  # ══════════════════════════════════════════════════════
  # INFRASTRUCTURE
  # ══════════════════════════════════════════════════════
  cosmos-emulator:
    image: mcr.microsoft.com/cosmosdb/linux/azure-cosmos-emulator:vnext-preview
    ports: ['8081:8081', '1234:1234']
    environment:
      PROTOCOL: http
      ENABLE_EXPLORER: 'true'
    restart: unless-stopped

  azurite:
    image: mcr.microsoft.com/azure-storage/azurite:3.35.0
    command: azurite-blob --blobHost 0.0.0.0 --blobPort 10000 --skipApiVersionCheck
    ports: ['10000:10000']
    volumes: [azurite-data:/data]
    restart: unless-stopped

  mailpit:
    image: axllent/mailpit:v1.27.5
    ports: ['1025:1025', '8025:8025']
    restart: unless-stopped

  traefik:
    image: traefik:v3.3
    command:
      - '--api.insecure=true'
      - '--providers.docker=true'
      - '--providers.docker.exposedbydefault=false'
      - '--entrypoints.web.address=:80'
    ports: ['80:80', '8080:8080']
    volumes: ['/var/run/docker.sock:/var/run/docker.sock:ro']
    restart: unless-stopped

  loki:
    image: grafana/loki:3.3.2
    ports: ['3100:3100']
    volumes: [loki-data:/loki]
    restart: unless-stopped

  grafana:
    image: grafana/grafana:11.4.0
    ports: ['3000:3000'] # NOTE: many Next.js webs also default to 3000 — avoid conflicts
    environment:
      GF_SECURITY_ADMIN_USER: admin
      GF_SECURITY_ADMIN_PASSWORD: lysnrai
    volumes: [grafana-data:/var/lib/grafana]
    restart: unless-stopped

  # ══════════════════════════════════════════════════════
  # GIT + CI + PACKAGE REGISTRY
  # ══════════════════════════════════════════════════════
  gitea:
    image: gitea/gitea:1.23
    ports: ['3300:3300', '2222:22']
    environment:
      GITEA__server__HTTP_PORT: 3300
      GITEA__server__ROOT_URL: http://gitea:3300
      GITEA__packages__ENABLED: 'true' # Built-in npm/container registry
      GITEA__service__DISABLE_REGISTRATION: 'true'
    volumes:
      - gitea-data:/data
    restart: unless-stopped

  act-runner:
    image: gitea/act_runner:latest
    environment:
      GITEA_INSTANCE_URL: http://gitea:3300
      GITEA_RUNNER_REGISTRATION_TOKEN: '<generate-from-gitea-admin>'
      GITEA_RUNNER_LABELS: 'ubuntu-latest:host'
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
    depends_on: [gitea]
    restart: unless-stopped

  # ══════════════════════════════════════════════════════
  # SHARED SERVICES (common-plat — no file: deps, pnpm workspace handles it)
  # ══════════════════════════════════════════════════════
  platform-service:
    build:
      context: ./learning_ai_common_plat
      dockerfile: services/platform-service/Dockerfile
    ports: ['4003:4003']
    env_file: [.env.ecosystem]
    environment:
      PORT: 4003
      COSMOS_AUTO_INIT: 'true'
    depends_on: [cosmos-emulator, azurite, mailpit]
    labels:
      - 'traefik.enable=true'
      - 'traefik.http.routers.platform.rule=Host(`platform.local`)'
      - 'traefik.http.services.platform.loadbalancer.server.port=4003'
    restart: unless-stopped

  extraction-service:
    build:
      context: ./learning_ai_common_plat
      dockerfile: services/extraction-service/Dockerfile
    ports: ['4005:4005']
    env_file: [.env.ecosystem]
    environment:
      PORT: 4005
    depends_on: [cosmos-emulator]
    restart: unless-stopped

  mcp-server:
    build:
      context: ./learning_ai_common_plat
      dockerfile: services/mcp-server/Dockerfile
    ports: ['4007:4007']
    env_file: [.env.ecosystem]
    environment:
      PORT: 4007
      PLATFORM_SERVICE_URL: http://platform-service:4003
      EXTRACTION_SERVICE_URL: http://extraction-service:4005
    depends_on: [platform-service, extraction-service]
    restart: unless-stopped

  # ══════════════════════════════════════════════════════
  # PRODUCT BACKENDS
  # ActionTrail + LocalMemGPT Dockerfiles use repo-root context.
  # Others use backend/ subdir context.
  # ══════════════════════════════════════════════════════
  lysnrai-backend:
    build: ./learning_voice_ai_agent/backend
    ports: ['4015:4015']
    env_file: [.env.ecosystem]
    environment: { PORT: '4015', SERVICE_NAME: lysnrai-backend }
    depends_on: [platform-service]
    restart: unless-stopped

  mindlyst-backend:
    build: ./learning_multimodal_memory_agents/backend
    ports: ['4014:4014']
    env_file: [.env.ecosystem]
    environment: { PORT: '4014', SERVICE_NAME: mindlyst-backend }
    depends_on: [platform-service]
    restart: unless-stopped

  chronomind-backend:
    build: ./learning_ai_clock/backend
    ports: ['4011:4011']
    env_file: [.env.ecosystem]
    environment: { PORT: '4011', SERVICE_NAME: chronomind-backend }
    depends_on: [platform-service]
    restart: unless-stopped

  jarvisjr-backend:
    build: ./learning_ai_jarvis_jr/backend
    ports: ['4012:4012']
    env_file: [.env.ecosystem]
    environment: { PORT: '4012', SERVICE_NAME: jarvisjr-backend }
    depends_on: [platform-service]
    restart: unless-stopped

  nomgap-backend:
    build: ./learning_ai_fastgap/backend
    ports: ['4013:4013']
    env_file: [.env.ecosystem]
    environment: { PORT: '4013', SERVICE_NAME: nomgap-backend }
    depends_on: [platform-service]
    restart: unless-stopped

  peakpulse-backend:
    build: ./learning_ai_peakpulse/backend
    ports: ['4010:4010']
    env_file: [.env.ecosystem]
    environment: { PORT: '4010', SERVICE_NAME: peakpulse-backend }
    depends_on: [platform-service]
    restart: unless-stopped

  flowmonk-backend:
    build:
      context: ./learning_ai_flowmonk
      dockerfile: backend/Dockerfile
    ports: ['4017:4017']
    env_file: [.env.ecosystem]
    environment: { PORT: '4017', SERVICE_NAME: flowmonk-backend }
    depends_on: [platform-service]
    restart: unless-stopped

  notelett-backend:
    build: ./learning_ai_notes/backend
    ports: ['4016:4016']
    env_file: [.env.ecosystem]
    environment: { PORT: '4016', SERVICE_NAME: notelett-backend }
    depends_on: [platform-service]
    restart: unless-stopped

  actiontrail-backend:
    build:
      context: ./learning_ai_trails # Dockerfile expects repo-root context
      dockerfile: backend/Dockerfile
    ports: ['4018:4018']
    env_file: [.env.ecosystem]
    environment: { PORT: '4018', SERVICE_NAME: actiontrail-backend }
    depends_on: [platform-service]
    restart: unless-stopped

  localmemgpt-backend:
    build:
      context: ./learning_ai_local_memory_gpt # Dockerfile expects repo-root context
      dockerfile: backend/Dockerfile
    ports: ['4019:4019']
    env_file: [.env.ecosystem]
    environment: { PORT: '4019', OLLAMA_URL: 'http://host.docker.internal:11434' }
    volumes: [localmemgpt-data:/app/db]
    restart: unless-stopped

  # ══════════════════════════════════════════════════════
  # WEB DASHBOARDS
  # IMPORTANT: Most webs default to port 3000 internally.
  # Use PORT env var to override, or remap via host:container ports.
  # ══════════════════════════════════════════════════════
  admin-web:
    build: ./learning_ai_common_plat/dashboards/admin-web
    ports: ['3001:3001']
    env_file: [.env.ecosystem]
    environment:
      PORT: 3001 # admin-web has NO port override — defaults to 3000 without this!
    depends_on: [platform-service]
    restart: unless-stopped

  user-dashboard:
    build: ./learning_voice_ai_agent/user-dashboard-web
    ports: ['3002:3002']
    env_file: [.env.ecosystem]
    depends_on: [lysnrai-backend]
    restart: unless-stopped

  tracker-web:
    build: ./learning_ai_common_plat/dashboards/tracker-web
    ports: ['3003:3003']
    env_file: [.env.ecosystem]
    depends_on: [platform-service]
    restart: unless-stopped

  nomgap-web:
    build: ./learning_ai_fastgap/web
    ports: ['3040:3040']
    environment:
      PORT: 3040
      NEXT_PUBLIC_NOMGAP_API_URL: http://nomgap-backend:4013/api
      NEXT_PUBLIC_PLATFORM_SERVICE_URL: http://platform-service:4003/api
    depends_on: [nomgap-backend]
    restart: unless-stopped

  actiontrail-web:
    build: ./learning_ai_trails/web
    ports: ['3060:3000'] # Internal 3000 → external 3060
    environment:
      NEXT_PUBLIC_API_URL: http://actiontrail-backend:4018
    depends_on: [actiontrail-backend]
    restart: unless-stopped

  localmemgpt-web:
    build:
      context: ./learning_ai_local_memory_gpt # Dockerfile expects repo-root context
      dockerfile: web/Dockerfile
    ports: ['3070:3070']
    environment:
      NEXT_PUBLIC_BACKEND_URL: http://localmemgpt-backend:4019
    depends_on: [localmemgpt-backend]
    restart: unless-stopped

  notelett-web:
    build: ./learning_ai_notes/web
    ports: ['3054:3000'] # Internal 3000 → external 3054
    environment:
      NEXT_PUBLIC_BACKEND_URL: http://notelett-backend:4016
    depends_on: [notelett-backend]
    restart: unless-stopped

  chronomind-web:
    build: ./learning_ai_clock/web
    ports: ['3051:3000'] # Internal 3000 → external 3051
    environment:
      NEXT_PUBLIC_BACKEND_URL: http://chronomind-backend:4011
      NEXT_PUBLIC_PLATFORM_SERVICE_URL: http://platform-service:4003
    depends_on: [chronomind-backend]
    restart: unless-stopped

  jarvisjr-web:
    build: ./learning_ai_jarvis_jr/web
    ports: ['3052:3000'] # Internal 3000 → external 3052
    environment:
      NEXT_PUBLIC_BACKEND_URL: http://jarvisjr-backend:4012
      NEXT_PUBLIC_PLATFORM_SERVICE_URL: http://platform-service:4003
    depends_on: [jarvisjr-backend]
    restart: unless-stopped

  flowmonk-web:
    build:
      context: ./learning_ai_flowmonk
      dockerfile: web/Dockerfile
    ports: ['3053:3000'] # Internal 3000 → external 3053
    environment:
      NEXT_PUBLIC_BACKEND_URL: http://flowmonk-backend:4017
      NEXT_PUBLIC_PLATFORM_SERVICE_URL: http://platform-service:4003
    depends_on: [flowmonk-backend]
    restart: unless-stopped

  mindlyst-web:
    build: ./learning_multimodal_memory_agents/mindlyst-native/web
    ports: ['3050:3050']
    environment:
      PORT: 3050 # package.json sets -p 3050
      NEXT_PUBLIC_BACKEND_URL: http://mindlyst-backend:4014
      NEXT_PUBLIC_PLATFORM_SERVICE_URL: http://platform-service:4003
    depends_on: [mindlyst-backend]
    restart: unless-stopped

volumes:
  azurite-data:
  loki-data:
  grafana-data:
  gitea-data:
  localmemgpt-data:

4.2 Phase 2 — Local Kubernetes (Docker Desktop or K3s)

Install K3s on the VM

# Install K3s (30 seconds, includes kubectl + containerd)
curl -sfL https://get.k3s.io | sh -

# Verify
sudo kubectl get nodes
# NAME       STATUS   ROLES                  AGE   VERSION
# myvm       Ready    control-plane,master   30s   v1.30.x+k3s1

# Copy kubeconfig for non-root usage
mkdir -p ~/.kube
sudo cp /etc/rancher/k3s/k3s.yaml ~/.kube/config
sudo chown $(id -u):$(id -g) ~/.kube/config

# Install Helm
curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash

Namespace Layout

kubectl create namespace bytelyst-infra      # Cosmos, Azurite, Mailpit, Loki, Grafana
kubectl create namespace bytelyst-platform   # platform-service, extraction, mcp
kubectl create namespace bytelyst-products   # 10 product backends
kubectl create namespace bytelyst-web        # All Next.js dashboards

Example K8s Manifest (one backend)

# k8s/products/lysnrai-backend.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: lysnrai-backend
  namespace: bytelyst-products
  labels:
    app: lysnrai-backend
    product: lysnrai
spec:
  replicas: 1 # Scale to 2+ when ready
  selector:
    matchLabels:
      app: lysnrai-backend
  template:
    metadata:
      labels:
        app: lysnrai-backend
    spec:
      containers:
        - name: lysnrai-backend
          image: bytelyst/lysnrai-backend:latest
          ports:
            - containerPort: 4015
          envFrom:
            - configMapRef:
                name: bytelyst-common-config
            - secretRef:
                name: bytelyst-secrets
          env:
            - name: PORT
              value: '4015'
            - name: SERVICE_NAME
              value: lysnrai-backend
          resources:
            requests:
              memory: '128Mi'
              cpu: '100m'
            limits:
              memory: '256Mi'
              cpu: '500m'
          livenessProbe:
            httpGet:
              path: /health
              port: 4015
            initialDelaySeconds: 10
            periodSeconds: 30
          readinessProbe:
            httpGet:
              path: /health
              port: 4015
            initialDelaySeconds: 5
            periodSeconds: 10
---
apiVersion: v1
kind: Service
metadata:
  name: lysnrai-backend
  namespace: bytelyst-products
spec:
  selector:
    app: lysnrai-backend
  ports:
    - port: 4015
      targetPort: 4015

Ingress (Traefik, built into K3s)

# k8s/ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: bytelyst-ingress
  namespace: bytelyst-products
  annotations:
    traefik.ingress.kubernetes.io/router.entrypoints: web
spec:
  rules:
    - host: lysnrai.local
      http:
        paths:
          - path: /api
            pathType: Prefix
            backend:
              service:
                name: lysnrai-backend
                port:
                  number: 4015
    - host: platform.local
      http:
        paths:
          - path: /
            pathType: Prefix
            backend:
              service:
                name: platform-service
                port:
                  number: 4003
    # ... repeat per product

---

5. Docker Compose → K3s Migration Cheat Sheet

Docker Compose	K3s Equivalent
services:	Deployment + Service
ports:	Service (ClusterIP/NodePort)
env_file:	ConfigMap + Secret
depends_on:	initContainers or readiness probes
volumes:	PersistentVolumeClaim (local-path)
restart: unless-stopped	Built-in (K8s always restarts pods)
labels: traefik.*	Ingress resource
docker compose up	kubectl apply -k k8s/
docker compose logs	kubectl logs -f deploy/X or Loki/Grafana
docker compose ps	kubectl get pods -A
Scale: change nothing	kubectl scale deploy/X --replicas=3

---

5.1 Scaling-Readiness

The ecosystem is designed for low-effort scaling:

• Service identity, ports, and inter-service URLs are env/config driven across all backends and dashboards
• Compose service boundaries map cleanly to Kubernetes Deployments + Services
• Traefik-based routing maps cleanly to Kubernetes Ingress resources
• The namespace split (infra, platform, products, web) provides a usable organizational model for K8s
• All repos consume @bytelyst/* from Gitea npm registry — Docker builds use standard pnpm install with BuildKit secret mount
• Moving from 1 replica to N replicas for stateless services is infra config work, not application rewrites
• Moving from Docker Desktop K8s to K3s or managed K8s reuses the same manifest model

Scaling the running system means

• Increasing replica counts
• Applying HPAs where justified
• Adjusting resource requests and limits
• Moving from local-path storage to persistent storage where needed
• Adding worker nodes or moving to a managed cluster

---

5.2 Remaining Work Before K3s Rollout

Validated ✅

• Host-side pnpm install against local Gitea registry (all 10 repos)
• Docker builds for all 10 repos with BuildKit secret mount
• 49 @bytelyst/* packages published to Gitea npm registry
• Local Gitea CI green for 8/8 repos with workflows
• Docker builds verified for MindLyst (backend + web) and LysnrAI (backend + dashboard)
• 1,591 backend tests passing, 9/9 web typechecks clean

K3s readiness — still needed

• Concrete K8s manifests or Helm values for the full ecosystem (not just examples)
• Image registry strategy for K3s (Gitea container registry or local import)
• Persistent volume strategy for Gitea, Grafana, Loki, Azurite, and stateful workloads
• Autoscaling thresholds and resource defaults validated under representative load
• Deployment ordering and health-check gating for infra → platform → products → web

---

6. K3s Practice Exercises (on single VM)

These exercises simulate real production scenarios:

Exercise 1: Rolling Update

# Build new image, deploy with zero downtime
docker build -t bytelyst/lysnrai-backend:1.1.0 ./learning_voice_ai_agent/backend
kubectl set image deploy/lysnrai-backend lysnrai-backend=bytelyst/lysnrai-backend:1.1.0 -n bytelyst-products
kubectl rollout status deploy/lysnrai-backend -n bytelyst-products

Exercise 2: Scale Horizontally

kubectl scale deploy/platform-service --replicas=3 -n bytelyst-platform
# Traefik auto-balances across all 3 pods

Exercise 3: ConfigMap / Secret Rotation

kubectl create secret generic bytelyst-secrets \
  --from-literal=JWT_SECRET='<replace-with-real-jwt-secret>' \
  --from-literal=COSMOS_KEY='<replace-with-real-cosmos-key>' \
  -n bytelyst-platform --dry-run=client -o yaml | kubectl apply -f -
kubectl rollout restart deploy -n bytelyst-platform

Exercise 4: Resource Limits + HPA

# Auto-scale platform-service 1→5 pods based on CPU
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: platform-service-hpa
  namespace: bytelyst-platform
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: platform-service
  minReplicas: 1
  maxReplicas: 5
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70

Exercise 5: Helm Chart (packaged deploy)

# Create chart scaffold
helm create bytelyst-ecosystem
# Templatize all 25+ services into one chart
# Deploy: helm install bytelyst ./bytelyst-ecosystem -n bytelyst

---

7. Scaling Path: Single VM → Multi-Node

Phase 1: Docker Compose          Phase 2: Local K8s (1 node)
┌─────────────────────┐          ┌──────────────────────────────┐
│  Single VM / Mac     │    →     │  Docker Desktop K8s (kind)   │
│  docker compose up   │          │  or K3s on Linux VM          │
│  ~25 containers      │          │  kubectl apply -k · ~25 pods │
└─────────────────────┘          └──────────────────────────────┘
                                          │
                                          ▼
Phase 3: K3s (3 nodes)           Phase 4: Managed K8s
┌──────────────────────┐         ┌──────────────────────┐
│  1 server + 2 agents │    →    │  AKS / EKS / GKE     │
│  Same manifests!     │         │  Same manifests!      │
│  Real HA             │         │  Auto-scaling nodes   │
└──────────────────────┘         └──────────────────────┘

Docker Desktop K8s → K3s migration: Same manifests, just change kubectl context.

Adding a worker node to K3s (Phase 3) is one command:

# On the worker VM:
curl -sfL https://get.k3s.io | K3S_URL=https://server-ip:6443 K3S_TOKEN=<token> sh -

---

8. Recommended Directory Structure

~/code/mygh/
├── docker-compose.ecosystem.yml     # Phase 1: all-in-one compose
├── .env.ecosystem                   # Shared env vars
├── k8s/                             # Phase 2: K3s manifests
│   ├── kustomization.yaml           # Kustomize root
│   ├── infra/                       # Cosmos emulator, Azurite, Mailpit, Loki, Grafana
│   ├── platform/                    # platform-service, extraction, mcp
│   ├── products/                    # 10 product backends
│   ├── web/                         # 10+ Next.js dashboards
│   ├── config/                      # ConfigMaps
│   └── secrets/                     # Secrets (gitignored)
├── helm/                            # Phase 3: Helm chart
│   └── bytelyst-ecosystem/
│       ├── Chart.yaml
│       ├── values.yaml
│       └── templates/
└── scripts/
    ├── ecosystem-up.sh              # docker compose -f docker-compose.ecosystem.yml up -d
    ├── ecosystem-k3s-deploy.sh      # kubectl apply -k k8s/
    └── ecosystem-build-all.sh       # Build all Docker images

---

9. Quick Start Commands

# ── Phase 1: Docker Compose ───────────────────────────
cd ~/code/mygh

# Build all images (first time, ~15-20 min)
docker compose -f docker-compose.ecosystem.yml build

# Start everything
docker compose -f docker-compose.ecosystem.yml up -d

# Check status
docker compose -f docker-compose.ecosystem.yml ps

# View logs
docker compose -f docker-compose.ecosystem.yml logs -f platform-service

# Tear down
docker compose -f docker-compose.ecosystem.yml down

# ── Phase 2a: Docker Desktop Kubernetes (Mac) ────────
# Enable K8s: Docker Desktop → Settings → Kubernetes → Enable
# Verify:
kubectl config use-context docker-desktop
kubectl get nodes    # Should show: docker-desktop   Ready   control-plane

# Build images (Docker Desktop shares images with K8s — no import needed!)
docker build -t bytelyst/platform-service:latest ./learning_ai_common_plat/services/platform-service

# Deploy all
kubectl apply -k k8s/

# Check pods
kubectl get pods -A

# Port-forward for local access
kubectl port-forward svc/platform-service 4003:4003 -n bytelyst-platform

# Or view everything in Docker Desktop GUI → Kubernetes tab

# ── Phase 2b: K3s (Linux VM) ─────────────────────────
# Build + load images into K3s containerd
docker build -t bytelyst/platform-service:latest ./learning_ai_common_plat/services/platform-service
sudo k3s ctr images import <(docker save bytelyst/platform-service:latest)

# Deploy (same manifests as Docker Desktop!)
kubectl apply -k k8s/
kubectl get pods -A

---

10. Dockerization Status

Repo	Backend Dockerfile	Web Dockerfile	output:'standalone'	Build Context	Status
LysnrAI	✅	✅ user-dashboard	✅ (conditional)	repo root	✅ Ready
MindLyst	✅	✅	✅ (conditional)	repo root	✅ Ready
ChronoMind	✅	✅	✅ (conditional)	backend/	✅ Ready
JarvisJr	✅	✅	✅ (conditional)	backend/	✅ Ready
PeakPulse	✅	— (no web)	—	backend/	✅ Ready
FlowMonk	✅	✅	✅ (conditional)	repo root	✅ Ready
NomGap	✅	✅	✅	backend/	✅ Ready
NoteLett	✅	✅	✅	backend/	✅ Ready
ActionTrail	✅	✅	✅	repo root	✅ Ready
LocalMemGPT	✅	✅	✅	repo root	✅ Ready
admin-web	—	✅ (in common-plat)	✅ (conditional)	pnpm workspace	✅ Ready
tracker-web	—	✅ (in common-plat)	✅ (conditional)	pnpm workspace	✅ Ready
user-dashboard	—	✅ (in common-plat)	✅ (conditional)	pnpm workspace	✅ Ready

All repos use pnpm with Gitea npm registry. Docker builds use BuildKit secret mount for registry auth.

---

11. Dockerfile Templates (reference)

All Dockerfiles use pnpm with BuildKit secret mount for Gitea npm registry authentication. next.config.ts MUST have output: 'standalone' for web Dockerfiles.

Backend template

FROM node:22-alpine AS builder
WORKDIR /app

RUN corepack enable && corepack prepare pnpm@10 --activate

COPY .npmrc.docker ./.npmrc
COPY package.json pnpm-lock.yaml ./
RUN --mount=type=secret,id=gitea_npm_token \
    export GITEA_NPM_TOKEN="$(cat /run/secrets/gitea_npm_token)" && \
    pnpm install --frozen-lockfile --ignore-scripts

COPY tsconfig.json ./
COPY src/ ./src/
RUN pnpm run build

FROM node:22-alpine
WORKDIR /app
ENV NODE_ENV=production

RUN corepack enable && corepack prepare pnpm@10 --activate

COPY .npmrc.docker ./.npmrc
COPY package.json pnpm-lock.yaml ./
RUN --mount=type=secret,id=gitea_npm_token \
    export GITEA_NPM_TOKEN="$(cat /run/secrets/gitea_npm_token)" && \
    pnpm install --frozen-lockfile --prod --ignore-scripts

COPY --from=builder /app/dist ./dist
COPY shared/product.json ../shared/product.json

EXPOSE ${PORT:-4010}
CMD ["node", "dist/server.js"]

Web (Next.js 16) template

FROM node:22-alpine AS builder
WORKDIR /app

RUN corepack enable && corepack prepare pnpm@10 --activate

COPY .npmrc.docker ./.npmrc
COPY package.json pnpm-lock.yaml ./
RUN --mount=type=secret,id=gitea_npm_token \
    export GITEA_NPM_TOKEN="$(cat /run/secrets/gitea_npm_token)" && \
    pnpm install --frozen-lockfile

COPY . .

ENV NEXT_TELEMETRY_DISABLED=1
RUN npx next build --webpack

FROM node:22-alpine
WORKDIR /app
ENV NODE_ENV=production

COPY --from=builder /app/.next/standalone ./
COPY --from=builder /app/.next/static ./.next/static
COPY --from=builder /app/public ./public

EXPOSE 3000
CMD ["node", "server.js"]

Docker build command

export TOKEN="$(cat ~/.gitea-npm-token)"
docker build \
  --add-host localhost:host-gateway \
  --build-arg GITEA_NPM_HOST=host.docker.internal \
  --secret id=gitea_npm_token,env=TOKEN \
  -f backend/Dockerfile \
  -t bytelyst/product-backend:latest \
  .

---

11.2 Gitea — Self-Hosted Git + CI + Package Registry

Gitea is a lightweight, self-hosted Git forge (~150 MB RAM) that provides three capabilities in one container:

1. Git hosting — mirror or primary for all 13+ repos
2. CI/CD — GitHub Actions–compatible workflows via act_runner
3. npm package registry — 49 @bytelyst/* packages published, consumed by all 10 product repos

Registry configuration

# .npmrc (host-side + Docker-side) — GITEA_NPM_HOST set by switch-network.sh
# NETWORK=corp → localhost, NETWORK=home → Azure VM (from ~/.gitea_vm_host)
@bytelyst:registry=http://${GITEA_NPM_HOST}:3300/api/packages/bytelyst/npm/
//${GITEA_NPM_HOST}:3300/api/packages/bytelyst/npm/:_authToken=${GITEA_NPM_TOKEN}

Publish workflow

# Build and publish all @bytelyst/* packages to Gitea
cd learning_ai_common_plat
pnpm -r --filter './packages/**' build
GITEA_NPM_TOKEN='<token>' bash ./scripts/gitea/publish-local-packages.sh

The publish helper uses pnpm pack first so workspace:* references are normalized before publishing to Gitea.

For full details, see GITEA_NPM_REGISTRY_MIGRATION.md.

Gitea Container Registry (bonus)

Gitea also provides a built-in Docker container registry at http://gitea:3300/v2/. Instead of building images and importing them into K3s, you can:

# Tag and push to Gitea container registry
docker tag bytelyst/platform-service:latest gitea:3300/bytelyst/platform-service:latest
docker push gitea:3300/bytelyst/platform-service:latest

# K3s / K8s pulls from Gitea registry
# In deployment YAML: image: gitea:3300/bytelyst/platform-service:latest

This gives you a complete self-hosted DevOps stack: Git → CI → npm registry → container registry → deploy.

---

12. Audit Findings (Review 2026-03-22)

Systematic code review of all claims in this document against the actual codebase.

F1. Port Conflicts (CRITICAL)

Grafana uses port 3000. The following webs also default to 3000:

• admin-web (no port in package.json)
• ChronoMind web (no port override)
• JarvisJr web (no port override)
• FlowMonk web (no port override)
• NoteLett web (Dockerfile EXPOSE 3000)
• ActionTrail web (Dockerfile EXPOSE 3000)

Fix: Set PORT env var in compose for each, or use host:container port remapping.

F2. @bytelyst/* Package Distribution for Docker Builds

All repos now consume @bytelyst/* from the Gitea npm registry (^0.1.0 semver refs). Docker builds use BuildKit secret mount for registry authentication.

Status: ✅ Resolved. See GITEA_NPM_REGISTRY_MIGRATION.md for details.

F3. NomGap Backend Dockerfile

Status: ✅ Fixed. Uses Gitea registry pattern.

F4. NoteLett Backend Dockerfile

Status: ✅ Fixed. Uses explicit COPY steps with Gitea registry pattern.

F5. Missing output: 'standalone' in next.config.ts (CRITICAL)

The Dockerfile template copies from .next/standalone/ — this directory only exists when output: 'standalone' is set in next.config.ts.

Web	Has output: 'standalone'?	Notes
NomGap	✅	Set directly
NoteLett	✅	Set directly
ActionTrail	✅	Set directly
LocalMemGPT	✅	Set directly
admin-web	✅	Conditional: process.env.VERCEL ? {} : { output: 'standalone' }
tracker-web	✅	Conditional (same)
user-dashboard	✅	Conditional (same)
ChronoMind	✅	Added 2026-03-22 (conditional)
JarvisJr	✅	Added 2026-03-22 (conditional)
FlowMonk	✅	Added 2026-03-22 (conditional)
MindLyst	✅	Added 2026-03-22 (conditional)

F6. Build Context Mismatch for ActionTrail + LocalMemGPT

Their Dockerfiles expect repo-root as build context (they COPY backend/... and COPY shared/...). The compose build: must use context: ./repo-name + dockerfile: backend/Dockerfile, not build: ./repo-name/backend.

Already correct in the compose above. Calling it out so future editors don't "simplify" it.

F7. Node.js Version Inconsistency

Existing Dockerfiles use mixed Node versions:

• NomGap, NoteLett: node:20-alpine
• ActionTrail, LocalMemGPT: node:22-alpine / node:22-slim

Recommendation: Standardize on node:22-alpine for all new Dockerfiles. Existing ones work but should be updated for consistency.

F8. Missing --webpack Flag for Next.js Builds

Several web apps require --webpack flag for builds (Serwist PWA incompatible with Turbopack, or @bytelyst/* file: ref transpilation). The Dockerfile template should call the repo's package-manager-appropriate build command (npm run build or pnpm run build) and that script should map to next build --webpack where required.

F9. Missing .env.ecosystem Template

The compose references .env.ecosystem. A working template now exists at the workspace root. Current key vars:

# .env.ecosystem — shared env for all services
COSMOS_ENDPOINT=http://cosmos-emulator:8081
COSMOS_KEY=<cosmos-emulator-key-or-local-placeholder>
COSMOS_DATABASE=bytelyst
DB_PROVIDER=cosmos
JWT_SECRET=dev-ecosystem-secret-do-not-use-in-production
AZURE_BLOB_CONNECTION_STRING=DefaultEndpointsProtocol=http;AccountName=devstoreaccount1;AccountKey=...;BlobEndpoint=http://azurite:10000/devstoreaccount1;
PLATFORM_SERVICE_URL=http://platform-service:4003
EXTRACTION_SERVICE_URL=http://extraction-service:4005
SMTP_HOST=mailpit
SMTP_PORT=1025
SMTP_FROM=noreply@bytelyst.local
FIELD_ENCRYPT_KEY_PROVIDER=env
FIELD_ENCRYPT_KEY=<64-char-hex-dev-key>
FIELD_ENCRYPT_MEK_NAME=dev-ecosystem-mek
NODE_ENV=production
CORS_ORIGIN=*
LOG_LEVEL=info

Status: ✅ Implemented as .env.ecosystem in the workspace root.

F10. host.docker.internal Only Works on Docker Desktop (Mac/Windows)

LocalMemGPT uses OLLAMA_URL: 'http://host.docker.internal:11434' — this works on Docker Desktop but not on Linux VMs (which is the likely deployment target).

Status: ✅ Addressed in docker-compose.ecosystem.yml via extra_hosts: ['host.docker.internal:host-gateway'] on localmemgpt-backend.

Summary of Audit Items

All audit findings have been resolved:

Priority	Item	Status
P0	All backend + web Dockerfiles created	✅ Done
P0	output: 'standalone' in all web configs	✅ Done
P0	Gitea npm registry for @bytelyst/* packages	✅ Done
P1	.env.ecosystem template	✅ Done
P2	Standardize Node.js version to 22-alpine	✅ Done
P2	extra_hosts for Linux VM Ollama access	✅ Done

---

13. Kubernetes Roadmap Reference

Kubernetes planning has been split into a standalone roadmap:

• docs/devops/KUBERNETES_ROADMAP.md

Use that document for:

• Docker Compose → Docker Desktop K8s / K3s transition planning
• K8s best practices and production comparison takeaways
• Helm values layering and namespace strategy
• secrets progression
• CI/CD guidance for chart/image promotion
• local K8s deployment workflow shape

SINGLE_VM_DEPLOYMENT.md remains the source of truth for:

• single-VM deployment scope
• Docker Compose ecosystem architecture
• Dockerization/package-manager deployment guidance
• current implementation status and audit findings

---

Summary

Question	Answer
Can deploy on single VM?	Yes. All ~35 containers fit in 32 GB RAM.
All Dockerized?	Yes. All 10 product repos + 3 shared services + 3 dashboards have Dockerfiles.
Package manager?	pnpm across all repos. @bytelyst/* packages from Gitea npm registry.
Self-hosted CI?	Yes. Gitea + act_runner (~250 MB combined). GitHub Actions–compatible workflows.
Package registry?	Gitea npm registry — 49 @bytelyst/* packages published. Also has Docker container registry.
K8s practice on single VM?	Docker Desktop K8s (Mac/Windows) or K3s (Linux). Same manifests scale to AKS/EKS/GKE.
Recommended VM?	8 vCPU / 32 GB (min) or 16 vCPU / 64 GB (with Ollama). Hetzner ~€45/mo for dev.
Enhanced tooling?	See SINGLE_VM_ENHANCED_PLAN.md — Coolify, Valkey, Uptime Kuma, SOPS.
Time to production K8s?	Phase 1 (compose) → Phase 2 (Docker Desktop / K3s) → Phase 3 (multi-node) → Phase 4 (managed). Same manifests.